The origination of mortgage loans and related products such as home equity lines of credit is highly complex in that it depends on the timely interactions of numerous participants such as brokers, lenders, appraisers, and insurers. These participants often are geographically dispersed, have varying levels of technology, are governed by different regulatory entities, and differ widely in scope and scale.
This complexity presents overwhelming challenges to the automation of loan origination through traditional methods and practices in information technology (IT), particularly workflow. Indeed the notion of a “flow” itself, the sequence of activities that unfolds in the processing of a loan, is non-deterministic, meaning that it cannot be determined in advance with a significant level of accuracy. This is due to the broad dependency of many activities on context—the values of external parameters at the moment the activity commences. An example is the willingness of a lender to relax a particular underwriting criterion based on how close it is to filling a commitment to deliver that type of loan to an investor.
In the last several decades there has been significant progress in the use of software to automate workflow, particularly in medium and large businesses. Classic examples of workflow come from manufacturing, where tasks are mostly physical and have straightforward sequencing.
The conceptual leap from automating workflow in physical domains such as manufacturing to workflow in more “virtual” domains such as financial services is moderately large but understandable for financial services having clear-cut processes. For example, it is straightforward to visualize a transaction such as a withdrawal from a checking account and how its component tasks (authentication of withdrawer, verification of sufficient funds, etc) are carried out by a combination of human and system workflow execution.
A simplifying assumption that has enabled much success in workflow automation is that the environment (the tasks, roles, events, messages, etc.) is fairly static and well-understood at the time the automation system is designed. When the environment is known, contained, and controllable, one can achieve significant automation benefits with a monolithic, client-server-based application running on a secure local network that achieves deployment and operational simplicity by imposing or assuming proprietary patterns on the environment. Further, at run-time, if the system can assume a deterministic sequence of activities at the outset of a workflow, some optimization can be made—though at the expense of flexibility to later incorporate new activities and flows.
However, in a more complex domain and environment such as mortgage origination, where tasks are distributed across multiple geographies, enterprises, and systems, and where no single entity has enough control to impose proprietary patterns, it is much more difficult to automate workflow. Traditionally, what has evolved is an environment where some level of automation has been achieved within individual enterprises but interaction with external firms is carried out through tenuous integrations or manual rekeying of data. There has been little success in achieving management or even mere statusing of participant firm workflows aggregated at an overall “meta workflow” level.
Therefore, there is a need for systems and methods for processing loan applications in complex environments, e.g., mortgage origination, in which the sequence of actions for processing the loan are not known in advance.